000			02246nam a22003137a 4500
003			OSt
005			20230921155611.0
008			230918b |||||||| |||| 00| 0 eng d
040			_aTUPM _beng _c- _erda
050			_aDIS T 185 _bG66 2023
100			_aGomez, Wilfred Ralph G.
245			_aAnfis-based control and digital process monitoring of food spray drying machine _c/Wilfred Ralph G. Gomez
264			_aManila _bTUP _c2023
300			_a204 pages : _bcolor illustration _c28 cm. _e+ 1 CD-ROM (4 ¾ in.)
336			_2rdacontent
337			_2rdamedia
338			_2rdacarrier
500			_aDissertation
502			_aCollege of Industrial Education-- _bDoctor of Technology _cTechnological University of the Philippines _d2023
520	3		_aSmart technologies disrupted the way the people live and helped solve 21st century problems in food manufacturing systems. In such case, these technologies can help preserve food products thru powderization by using Spray-drying machines. However, existing control methods like PID algorithm built-in in these Spray-drying machines proposes several issues in maintaining high accuracy in developing new native products that results in high-quality powders. Said problems prompted this study at designing an ANFIS-Based Control System and Digital Process Monitoring for Food Spray Drying Machine. Results revealed that testing the hardware components, designing a User Interface (UI), and adopting software components such as ANFIS Machine Learning contributed to having high accuracy in controlling the inlet temperature, feedrate, blower fan speed, and the needle speed. In addition, said activities combined with using the digital process monitoring feature unanimously improved the user's perceived smartness and usability of the prototype. This implies that the prototype can be effectively used to powderize native products which may significantly contribute to nation building-Author's abstract.
650			_2Adaptive control system
650			_2Usability
653			_aProportional Integral Derivative (PIP)
653			_aAdaptive Neuro-Fuzzy Inference System (ANFIS)
653			_aSpray Drying machine
942			_2ddc _cDIS _n0
999			_c28198 _d28198